Project Title

QoS Buddy

Overview

This project is an agent-based network traffic simulation designed to model and analyze data flow in a virtual environment (e.g., a smart town or warehouse system). It simulates user behavior, network congestion, and Quality of Service (QoS) metrics, with the goal of enabling prediction and optimization using machine learning.

Features

• Agent-based simulation of network traffic
• Real-time congestion modeling
• QoS metrics tracking (latency, throughput, packet loss)
• Scenario-based simulation (varying loads and behaviors)
• Data collection for analysis and model training
• Machine learning integration for prediction and optimization

Tech Stack

Frontend

• Dashboard for visualization (charts, metrics, simulation state)
• Tools: React, Plotly (or similar)

Backend

• Simulation engine (agent logic + environment)
• Data processing and storage
• ML model integration
• Tools: Python, Flask/FastAPI, TensorFlow/PyTorch

Architecture

The system follows a modular architecture:

• Agents Layer: Simulates users/devices generating traffic
• Environment Layer: Models the network (nodes, links, congestion)
• Simulation Engine: Handles interactions and time-based events
• Data Layer: Collects and stores QoS metrics
• ML Layer: Predicts congestion and suggests optimizations
• Visualization Layer: Displays results and insights

Contributors

• Haroun Zriba
• Muaadh AlSoumhi
• Rayen Krimi
• Omar Mezoughi
• Mohamed Ayman Hamzaoui
• Fares Hasni
• Zayneb Maatoug

Academic Context

This project was developed as part of the PIDS – 4th Year Engineering Program at Esprit School of Engineering (Academic Year 2025–2026).

Getting Started

Prerequisites

• Python 3.10+

Installation

git clone https://github.com/zrharounesprit/Esprit-PIDS-4DS1-2026-QoSBuddy.git
cd Esprit-PIDS-4DS1-2026-QoSBuddy
pip install -r requirements.txt

Running

Start each API in a separate terminal, then launch the dashboard:

# Terminal 1 — Anomaly Detection API
uvicorn utils.anomaly_api:app --host 127.0.0.1 --port 8001

# Terminal 2 — Root Cause Analysis API
uvicorn utils.main_RCA:app --host 127.0.0.1 --port 8002

# Terminal 3 — SLA Detection API
uvicorn utils.sla_api:app --host 127.0.0.1 --port 8003

# Terminal 4 — Streamlit Dashboard
streamlit run app.py